Risk prediction on a peer-to-peer network

ABSTRACT

A method for warning an agent is presented. The method includes determining a probability for each one of a set of potential second behaviors of a first agent based on observed first behavior of the first agent at a first time period. The method also includes observing a second behavior of the first agent a second time period. The method further includes determining whether the observed second behavior corresponds to a potential second behavior with a probability that is less than a threshold. The method still further includes transmitting a warning to a second agent when the probability is less than the threshold.

BACKGROUND Field

Certain aspects of the present disclosure generally relate to vehiclewarning systems.

Background

A vehicle warning system may warn a driver of a potentially dangeroussituation. In response to the warning, the driver may initiateprecautionary measures to avoid the potentially dangerous situation. Thevehicle warning systems may sense a potentially dangerous situationbased on information obtained from one or more vehicle sensors. In somecases, a potentially dangerous situation may not be visible to thevehicle's sensors. It is desirable to improve vehicle warning systems toprovide warnings for potentially dangerous situations that are notvisible to the vehicle's sensors.

SUMMARY

In one aspect of the present disclosure, a method for warning an agentis disclosed. The method includes determining a probability for each oneof a set of potential second behaviors of a first agent based on anobserved first behavior of the first agent at a first time period. Themethod also includes observing a second behavior of the first agent at asecond time period. The method further includes determining whether theobserved second behavior corresponds to a potential second behavior witha probability that is less than a threshold. The method still furtherincludes transmitting a warning to a second agent when the probabilityis less than the threshold.

In another aspect of the present disclosure, a non-transitorycomputer-readable medium with non-transitory program code recordedthereon is disclosed. The program code is for warning an agent. Theprogram code is executed by a processor and includes program code todetermine a probability for each one of a set of potential secondbehaviors of a first agent based on an observed first behavior of thefirst agent at a first time period. The program code also includesprogram code to observe a second behavior of the first agent at a secondtime period. The program code further includes program code to determinewhether the observed second behavior corresponds to a potential secondbehavior with a probability that is less than a threshold. The programcode still further includes program code to transmit a warning to asecond agent when the probability is less than the threshold.

Another aspect of the present disclosure is directed to an apparatus forwarning an agent. The apparatus having a memory and one or moreprocessors coupled to the memory. The processor(s) is configured todetermine a probability for each one of a set of potential secondbehaviors of a first agent based on an observed first behavior of thefirst agent at a first time period. The processor(s) is also configuredto observe a second behavior of the first agent at a second time period.The processor(s) is further configured to determine whether the observedsecond behavior corresponds to a potential second behavior with aprobability that is less than a threshold. The processor(s) stillfurther configured to transmit a warning to a second agent when theprobability is less than the threshold.

This has outlined, rather broadly, the features and technical advantagesof the present disclosure in order that the detailed description thatfollows may be better understood. Additional features and advantages ofthe present disclosure will be described below. It should be appreciatedby those skilled in the art that this present disclosure may be readilyutilized as a basis for modifying or designing other structures forcarrying out the same purposes of the present disclosure. It should alsobe realized by those skilled in the art that such equivalentconstructions do not depart from the teachings of the present disclosureas set forth in the appended claims. The novel features, which arebelieved to be characteristic of the present disclosure, both as to itsorganization and method of operation, together with further objects andadvantages, will be better understood from the following descriptionwhen considered in connection with the accompanying figures. It is to beexpressly understood, however, that each of the figures is provided forthe purpose of illustration and description only and is not intended asa definition of the limits of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present disclosure willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify correspondingly throughout.

FIG. 1 illustrates an example of a vehicle in an environment accordingto aspects of the present disclosure.

FIGS. 2A, 2B, and 2C illustrate examples of identifying risky behavioraccording to aspects of the present disclosure.

FIG. 3 illustrates a flow diagram for a warning system according toaspects of the present disclosure.

FIG. 4 is a diagram illustrating an example of a hardware implementationfor a warning system according to aspects of the present disclosure.

FIG. 5 illustrates a flow diagram for a method of transmitting a warningaccording to aspects of the present disclosure.

DETAILED DESCRIPTION

The detailed description set forth below, in connection with theappended drawings, is intended as a description of variousconfigurations and is not intended to represent the only configurationsin which the concepts described herein may be practiced. The detaileddescription includes specific details for the purpose of providing athorough understanding of the various concepts. It will be apparent tothose skilled in the art, however, that these concepts may be practicedwithout these specific details. In some instances, well-known structuresand components are shown in block diagram form in order to avoidobscuring such concepts.

A vehicle warning system may warn an agent of a potentially dangeroussituation. The potentially dangerous situation may be a situation thatcan harm the vehicle and/or occupants, such as a road hazard or anerratic agent (e.g., another vehicle or pedestrian). For example, thevehicle warning system may generate a warning when a distance betweenthe vehicle and an object is less than a threshold. A driver and/orautonomous system may take precautionary measures to avoid thepotentially dangerous situation. According to aspects of the presentdisclosure, the agent may operate in a manual mode, an autonomous mode,and/or a semi-autonomous mode.

In the manual mode, a human driver manually operates (e.g., controls)the vehicle. In the autonomous mode, a vehicle control system operatesthe vehicle without human intervention. In the semi-autonomous mode, thehuman may operate the vehicle, and the vehicle control system mayoverride or assist the human. For example, the vehicle control systemmay override the human to prevent a collision or to obey one or moretraffic rules.

Vehicle warning systems may sense potentially dangerous situations basedon information obtained from one or more vehicle sensors. The sensorsmay include, for example, a red-green-blue (RGB) camera, a RADAR sensor,and/or a LIDAR sensor. In some cases, the potentially dangeroussituation may not be visible to the vehicle's sensors. For example, thevehicles' sensors may not be within visual range of the potentiallydangerous situation. As another example, an object on the road may blocka view of one or more sensors. It is desirable to improve vehiclewarning systems to provide warnings for potentially dangerous situationsthat are not visible to the vehicle's sensors.

Aspects of the present disclosure are directed to identifying an agentengaging in risky behavior (e.g., a risky agent). Aspects of the presentdisclosure are also directed to transmitting a warning regarding therisky agent. The warning may warn other agents when the other agents'sensors are not within visual range of the risky agent. In response tothe warning, the other agents may initiate defensive measures to reducethe probability of potential harm.

FIG. 1 illustrates an example of an ego vehicle 100 (e.g., ego agent) inan environment 150 according to aspects of the present disclosure. Asshown in FIG. 1, the ego vehicle 100 is traveling on a road 110. A firstvehicle 104 (e.g., other agent) may be ahead of the ego vehicle 100, anda second vehicle 116 may be adjacent to the ego vehicle 100. In thisexample, the ego vehicle 100 may include a 2D camera 108, such as a 2DRGB camera, and a LIDAR sensor 106. Other sensors, such as RADAR and/orultrasound, are also contemplated. Additionally, or alternatively, theego vehicle 100 may include one or more additional 2D cameras and/orLIDAR sensors. For example, the additional sensors may be side facingand/or rear facing sensors.

In one configuration, the 2D camera 108 captures a 2D image thatincludes objects in the 2D camera's 108 field of view 114. The LIDARsensor 106 may generate one or more output streams. The first outputstream may include a 3D cloud point of objects in a first field of view,such as a 360° field of view 112 (e.g., bird's eye view). The secondoutput stream 124 may include a 3D cloud point of objects in a secondfield of view, such as a forward facing field of view.

The 2D image captured by the 2D camera includes a 2D image of the firstvehicle 104, as the first vehicle 104 is in the 2D camera's 108 field ofview 114. As is known to those of skill in the art, a LIDAR sensor 106uses laser light to sense the shape, size, and position of objects in anenvironment. The LIDAR sensor 106 may vertically and horizontally scanthe environment. In the current example, an artificial neural network(e.g., autonomous driving system) of the ego vehicle 100 may extractheight and/or depth features from the first output stream. Theautonomous driving system of the ego vehicle 100 may also extract heightand/or depth features from the second output stream.

The information obtained from the sensors 106, 108 may be used tonavigate the ego vehicle 100 along a route when the ego vehicle 100 isin an autonomous mode. The sensors 106, 108 may be powered fromelectricity provided from the vehicle's 100 battery (not shown). Thebattery may also power the vehicle's motor. The information obtainedfrom the sensors 106, 108 may also identify risky agents.

Agents, such as vehicles, pedestrians, and bicyclists, may engage invarious types of risky behavior. The risky behavior may include, forexample, swerving between lanes or failing to obey traffic laws. An egoagent may observe the agent's behavior via one or more sensors. Inresponse to identifying the risky behavior, the ego agent may engagedefensive measures to avoid an incident. For brevity, the agentperforming the risky behavior may be referred to as the risky agent.

Other agents in proximity to the risky agent may fail to observe therisky agent. As such, the other agents may not have time to engagedefensive measures before the risky agent is within visual range. Asdiscussed, the agent may fail to observe the risky agent due to, forexample, sensor occlusion, a distance from the risky agent, and/or otherreasons.

In one configuration, an ego agent's warning system identifies a riskyagent and transmits a risky behavior warning to other agents. Thewarning may be targeted to specific agents, such as agents within aspecific proximity. As another example, the ego agent may broadcast thewarning, such that the warning is not targeted to specific agents. Uponreceiving the warning, the other agents may perform defensive actions.The other agents may include, for example, vehicles, pedestrians, and/orbicyclists.

The ego agent identifies an agent performing a risky behavior byobserving the agent's behavior. In one configuration, after observing anagent's behavior (e.g., actions) during a time period, the ego agentassigns probabilities to each behavior of a set of possible behaviors ata subsequent time period. The probability is the probability of theagent performing the behavior given the current conditions. The egoagent may determine that the agent's behavior is risky when theprobability of the agent's behavior is less than a threshold. This typeof behavior may also be referred to as a low probability behavior.

FIG. 2A illustrates an example of observing an agent's behavioraccording to aspects of the present disclosure. As shown in FIG. 2A,sensors (not shown) of an ego agent 200 observe a first agent 202traveling on a first road 216. The sensor data may reflect a positionand a velocity of the first agent 202. As such, the ego agent's 200warning system determines that the first agent 202 is traveling towardsan intersection 226. Based on the sensor data, the warning system mayalso determine that a traffic light 210 at the intersection 226 hasswitched from a green light 218 to a yellow light 212.

The warning system may be an artificial neural network that has beentrained to determine road conditions and agent information. Roadconditions may include traffic conditions, traffic signal status (e.g.,red, yellow, green), posted speed limits, and/or other information.Agent information may include an agent's position, velocity, directionof travel, and/or other information. The warning system may also receiveenvironmental information, such as speed limits, road layoutinformation, weather, and/or other information, from a remote device(e.g., cloud-based server).

In the example of FIG. 2A, a second agent 204 is traveling towards theintersection 210 on a second road 214 adjacent to the first road 216. Inthis example, a portion 208A of the second agent's 204 sensor field ofview 208 is blocked by a third agent 206. In one configuration, thesecond agent 204 broadcasts a message notifying one or more agents 200,202, 206 of the partially blocked view. The message may be transmittedvia a vehicle to vehicle (V2V) network (e.g., peer-to-peer network).Additionally, or alternatively, the message may be transmitted via aninfrastructure network (e.g., wireless communication network), a vehicleto everything (V2X) network, a vehicle to infrastructure (V2I) network,a vehicle to network (V2N) network, a vehicle to pedestrian (V2P)network, and/or another type of network.

After observing the first agent's 202 behavior (e.g., position,velocity, direction of travel, etc.), the ego agent 200 determinesprobabilities for each behavior of a set of potential behaviors for asubsequent time period (e.g., future behaviors). FIG. 2B illustrates anexample of determining probabilities for each behavior of a set ofpossible future behaviors according to aspects of the presentdisclosure. In this example, the ego agent's 200 warning systemdetermines a set of potential future behaviors based on the firstagent's 202 current behavior (e.g., traveling towards the intersection226).

As shown in FIG. 2B, the set of potential future behaviors include, forexample, proceeding through the intersection 222 before or after thetraffic light 210 turns to red 220, and stopping at the intersection224. For brevity, in the current example, the set of potential futurebehaviors is limited to the aforementioned behaviors. Other potentialfuture behaviors are possible.

The ego agent's 200 warning system assigns a probability to eachpotential future behavior 222, 224. For example, the ego agent's 200warning system assigns a high probability value to the potentialbehavior of stopping at the intersection 224. The high probability valuerefers to a value that is greater than or equal to a first threshold. Inthis example, the ego agent's 200 warning system assigns a lowprobability to the potential behavior of proceeding through theintersection 222. The low probability value refers to a value that isless than or equal to a second threshold.

The probabilities may be assigned based on training. For example, thewarning system may be trained on behavior data of various agents. Thetraining may be updated as the warning system observes real-worldbehaviors. The behavior data may include high probability behaviors andlow probability behaviors. High probability behaviors generally relateto behaviors in accordance with traffic regulations, such as, driving ona correct side of a road and following a posted speed limit. Lowprobability behaviors generally relate to behaviors that are not inaccordance with traffic regulations, such as, driving on a wrong side ofa road, exceeding a posted speed limit, and swerving between lanes.

After determining the probabilities of the first agent's 202 potentialfuture behaviors 222, 224, the ego agent 200 continues to monitor thefirst agent's 202 behavior. In one configuration, the warning systemtransmits a warning if the first agent 202 performs a low probabilitybehavior. FIG. 2C illustrates an example of transmitting a warning 228according to aspects of the present disclosure.

As shown in FIG. 2C, the first agent 202 has entered the intersection226 after the traffic light 210 turns red 220. In response, the egoagent's 200 warning system determines that the first agent 202 hasengaged in a low probability behavior. As discussed, the low probabilitybehavior may be classified as risky behavior. Therefore, the warningsystem transmits a warning 228 to other agents 204, 206. In the presentexample, the first agent 202 is still not visible to the second agent204 because the third agent 206 is blocking a portion 208A of the secondagent's 204 field of view 208.

The warning 228 may be broadcast to all agents 204, 206 within range ofthe ego agent 200. Additionally, or alternatively, the warning 228 maybe specifically transmitted to one or more agents. For example, thewarning 228 may only be transmitted to the second agent 204 based on thesecond agent's 204 message indicating the occluded field of view 208.The warning 228 may be transmitted via a V2V network. Additionally, oralternatively, the message may be transmitted via an infrastructurenetwork, a V2X, a V2I network, a V2N network, a V2P, and/or another typeof network.

The driver of the second agent 204 may be notified of the warning 228via an in-cabin output, such as an audio message or a message displayedon a user interface. As an example, the message is displayed on aheads-up display or a head unit of the second agent 204. In addition tonotifying the driver of the risky behavior, the message may include oneor more of a make, model, vehicle class, or color of the first agent202, and a description of the risky behavior.

An autonomous or semi-autonomous driving system of the second agent mayalso process the warning 228. The second agent's 204 driving systemand/or driver may initiate a defensive driving mode in response to thewarning 228. For example, the second agent's 204 driving system and/ordriver may change velocity and/or trajectory (e.g., slow down or stopbefore entering the intersection 226). Additionally, the ego agent's 200driving system and/or driver may initiate a defensive driving mode inresponse to identification of the risky behavior.

In one configuration, the warning 228 is also transmitted to the firstagent 202 (e.g., the agent that is performing the risky behavior). Inresponse to receiving the warning 228, the first agent's 202 drivingsystem and/or driver may stop engaging in the risky behavior.Additionally, or alternatively, the first agent's 202 driving systemand/or driver may initiate a defensive driving mode to reduce aprobability of a potentially dangerous situation.

According to aspects of the present disclosure, if a vehicle isoperating in a manual mode, an autonomous or semi-autonomous drivingmode may be initiated in response to the warning 228. The autonomous orsemi-autonomous driving mode may overtake the manual operation or assistthe manual operation in averting a potentially dangerous situation, suchas a collision.

In one configuration, the ego agent predicts a risky behavior beforeobserving the agent engaging in the risky behavior. For example, basedon the sensor data, the ego agent may predict that the agent will engagein the low probability behavior (e.g., risky behavior). The ego agentmay transmit the warning based on the predicted risky behavior.

For example, based on the example of FIG. 2C, the ego agent 200 maydetermine that the first agent 202 is traveling at 60 miles per hour.The ego agent 200 determines the distance for a complete stop given thefirst agent's 202 velocity. The distance may be an average distance orbased on data obtained for the first agent's 202 make and model. Fromthe sensor data, the ego agent 200 may determine that a distance betweenthe first agent 202 and the intersection 226 is less than the determinedstopping distance. Accordingly, the ego agent 200 may predict that thefirst agent 202 cannot, or will not, stop before entering theintersection 226.

In one configuration, the ego agent uses lane geometry for riskestimation. The lane geometry refers to estimating a correct directionof travel for agents in a given lane. Based on the estimated directionof travel, the ego agent predicts whether a given agent is traveling ina correct direction for a given lane.

The lane geometry may be the geometry of the road on which the ego agentis traveling. As an example, the ego agent observes an agent travelingin a lane. Risky behavior may be predicted if the agent is traveling ina direction that is opposite to the direction used to estimate the lanegeometry.

As discussed, agents in an environment may transmit and/or receivemessages via a V2V network, an infrastructure network, a V2X network, aV2I network, a V2N network, a V2P network, and/or another type ofnetwork. V2N and V2V networks may connect agents to cloud services andwireless infrastructure (e.g., LTE, 5G, etc.). For example, real-timeinformation about traffic, routes, and/or road situations may becommunicated over the V2N and V2V networks. The agents may also transmitinformation agent-centric information, such as, an agent's location,direction, speed, braking status, and/or steering wheel position.

In a V2I network, agents may communicate with road infrastructure, suchas traffic signals. V2I sensors collect information about traffic,traffic light states, radar devices, cameras, and other road signals.Nodes in a V2I network may work together to improve throughput. In a V2Pnetwork, agents may communicate with pedestrians. For example, a vehiclemay transmit a message to a pedestrian's mobile device. In a V2Xnetwork, the agent may collect and share information with any other node(e.g., network, person, infrastructure) in the environment. That is, V2Xnetworks may be a combination of all other types of agent communication.

FIG. 3 illustrates a flow diagram 300 for transmitting a risky behaviorwarning according to aspects of the present disclosure. As shown in FIG.3, at block 302, an ego agent observes an agent's behavior (e.g.,action) during a first time period. An agent may be a vehicle,pedestrian, bicyclist, or another type of mechanized equipment (e.g., anelectric scooter) in an environment.

The agent's behavior may be observed via one or more sensors of the egoagent. That is, an ego agent's warning system receives and interpretssensor data. The warning system may be an artificial neural network.Additionally, or alternatively, the ego agent may receive informationregarding the agent's behavior from one or more infrastructure sensors,such as a camera on a traffic signal.

At block 304, the ego agent predicts a set of potential future behaviorsfor the agent based on the observed actions. The set of potential futurebehaviors are potential behaviors that may occur at a second time periodthat is after the first time period. The potential future behaviors maybe predicted based on training data provided to the warning system.

As an example, the ego agent may observe an agent enabling a left turnsignal and entering a left turn lane before an intersection. Based onthe observations, the ego agent predicts that the agent will turn leftat the intersection. The ego agent may also predict other potentialbehaviors for the agent, such as swerving right or continuing straightthrough the intersection.

At block 306, the ego agent assigns a probability to each potentialbehavior. The warning system may be trained to determine theprobabilities from the behavior data. The probabilities may be assignedbased on training on behavior data. The behaviors may be categorized ashigh probability behaviors and low probability behaviors. Other behaviorcategories, such as medium probability, may also be used. In theprevious example, the potential behavior of turning left at theintersection is a high probability behavior. Additionally, the potentialbehaviors of swerving right and continuing straight through theintersection are low probability behaviors.

A high probability behavior refers to behavior with a probability valuethat is greater than or equal to a high probability threshold. A lowprobability behavior refers to behavior with a probability value that isless than or equal to a low probability threshold. The thresholds may bepre-set by a user or a manufacturer. Additionally, or alternatively, thethresholds may be dynamically adjusted based on driving conditions,environment, user preference, and/or other factors.

At block 308, the ego agent observes the agent's behavior at a secondtime period. Based on the observations at the second time period, atblock 312, the ego agent determines if the probability assigned to theobserved behavior is less than a low probability threshold. That is, theego agent determines whether the agent is engaged in a low probabilitybehavior (e.g., risky behavior).

As discussed, the ego agent may predict a risky behavior beforeobserving the agent engaging in the risky behavior. For example, basedon the sensor data at the second time period, the ego agent may predictthat the agent has an increased potential of performing the lowprobability behavior (e.g., risky behavior). Thus, based on the sensordata, the ego agent may observe the agent performing the risky behavioror predict an occurrence of the risky behavior.

If the agent is engaged in a low probability behavior, at block 314, theego agent warns agents of the risky behavior. The warning may betransmitted and/or received via one or more networks. The warning may bebroadcast to agents within a certain range of the ego agent or targetedto one or more agents. The agent performing the risky behavior mayreceive the warning from the ego agent. An agent's driving system and/ordriver may engage a defensive driving mode in response to receiving thewarning.

In an optional configuration, at block 318, the ego agent's drivingsystem engages the defensive driving mode in response to transmittingthe warning. If the agent is not engaged in a low probability behavior,at block 316, the ego agent's driving system continues operating in acurrent operating mode. As discussed, the agent may operate in a manualmode, an autonomous mode, and/or a semi-autonomous mode.

FIG. 4 is a diagram illustrating an example of a hardware implementationfor a warning system 400, according to aspects of the presentdisclosure. The warning system 400 may be a component of a vehicle, arobotic device, or another device. For example, as shown in FIG. 4, thewarning system 400 is a component of a vehicle 428. Aspects of thepresent disclosure are not limited to the warning system 400 being acomponent of the vehicle 428, as other types of agents, such as a bus,boat, drone, or robot, are also contemplated for using the warningsystem 400.

The vehicle 428 may operate in one or more of an autonomous operatingmode, a semi-autonomous operating mode, and a manual operating mode.Furthermore, the vehicle 428 may be an electric vehicle, a hybridvehicle, a fuel vehicle, or another type of vehicle.

The warning system 400 may be implemented with a bus architecture,represented generally by a bus 440. The bus 440 may include any numberof interconnecting buses and bridges depending on the specificapplication of the warning system 400 and the overall designconstraints. The bus 440 links together various circuits including oneor more processors and/or hardware modules, represented by a processor420, a communication module 422, a location module 418, a sensor module402, a locomotion module 426, a navigation module 424, and acomputer-readable medium 414. The bus 440 may also link various othercircuits such as timing sources, peripherals, voltage regulators, andpower management circuits, which are well known in the art, andtherefore, will not be described any further.

The warning system 400 includes a transceiver 416 coupled to theprocessor 420, the sensor module 402, a risky behavior module 408, thecommunication module 422, the location module 418, the locomotion module426, the navigation module 424, and the computer-readable medium 414.The transceiver 416 is coupled to an antenna 444. The transceiver 416communicates with various other devices over one or more communicationnetworks, such as an infrastructure network, a V2V network, a V2Inetwork, a V2X network, a V2P network, or another type of network. As anexample, the transceiver 416 may transmit a warning to other agents whenthe risky behavior module 408 identifies a risky agent.

The warning system 400 includes the processor 420 coupled to thecomputer-readable medium 414. The processor 420 performs processing,including the execution of software stored on the computer-readablemedium 414 providing functionality according to the disclosure. Thesoftware, when executed by the processor 420, causes the warning system400 to perform the various functions described for a particular device,such as the vehicle 428, or any of the modules 402, 408, 414, 416, 418,420, 422, 424, 426. The computer-readable medium 414 may also be usedfor storing data that is manipulated by the processor 420 when executingthe software.

The sensor module 402 may be used to obtain measurements via differentsensors, such as a first sensor 406 and a second sensor 404. The firstsensor 406 may be a vision sensor, such as a stereoscopic camera or ared-green-blue (RGB) camera, for capturing 2D images. The second sensor404 may be a ranging sensor, such as a light detection and ranging(LIDAR) sensor or a radio detection and ranging (RADAR) sensor. Ofcourse, aspects of the present disclosure are not limited to theaforementioned sensors as other types of sensors, such as, for example,thermal, sonar, and/or lasers are also contemplated for either of thesensors 404, 406.

The measurements of the first sensor 406 and the second sensor 404 maybe processed by one or more of the processor 420, the sensor module 402,the risky behavior module 408, the communication module 422, thelocation module 418, the locomotion module 426, the navigation module424, in conjunction with the computer-readable medium 414 to implementthe functionality described herein. In one configuration, the datacaptured by the first sensor 406 and the second sensor 404 may betransmitted to an external device via the transceiver 416. The firstsensor 406 and the second sensor 404 may be coupled to the vehicle 428or may be in communication with the vehicle 428.

The location module 418 may be used to determine a location of thevehicle 428. For example, the location module 418 may use a globalpositioning system (GPS) to determine the location of the vehicle 428.The communication module 422 may be used to facilitate communicationsvia the transceiver 416. For example, the communication module 422 maybe configured to provide communication capabilities via differentwireless protocols, such as WiFi, long term evolution (LTE), 4G, etc.The communication module 422 may also be used to communicate with othercomponents of the vehicle 428 that are not modules of the warning system400.

The locomotion module 426 may be used to facilitate locomotion of thevehicle 428. As an example, the locomotion module 426 may control amovement of the wheels. As another example, the locomotion module 426may be in communication with one or more power sources of the vehicle428, such as a motor and/or batteries. Of course, aspects of the presentdisclosure are not limited to providing locomotion via wheels and arecontemplated for other types of components for providing locomotion,such as propellers, treads, fins, and/or jet engines.

The warning system 400 also includes the navigation module 424 forplanning a route or controlling the locomotion of the vehicle 428, viathe locomotion module 426. In one configuration, the navigation module424 engages a defensive driving mode when the risky behavior module 408identifies a risky agent. The navigation module 424 may override userinput when the user input is expected (e.g., predicted) to cause acollision. The modules may be software modules running in the processor420, resident/stored in the computer-readable medium 414, one or morehardware modules coupled to the processor 420, or some combinationthereof.

The risky behavior module 408 may be in communication with the sensormodule 402, the transceiver 416, the processor 420, the communicationmodule 422, the location module 418, the locomotion module 426, thenavigation module 424, and the computer-readable medium 414. In oneconfiguration, the risky behavior module 408 receives sensor data fromthe sensor module 402. The sensor module 402 may receive the sensor datafrom the first sensor 406 and the second sensor 404. According toaspects of the present disclosure, the sensor module 402 may filter thedata to remove noise, encode the data, decode the data, merge the data,extract frames, or perform other functions. In an alternateconfiguration, the risky behavior module 408 may receive sensor datadirectly from the first sensor 406 and the second sensor 404.

In one configuration, the risky behavior module 408 determines aprobability for each one of a set of potential second behaviors of afirst agent based on an observed first behavior of the first agent at afirst time period. The first behaviors may be observed via the firstsensor 406 and/or the second sensor 404. The potential second behaviorsmay be determined (e.g., predicted) based on the sensor data. The firstsensor 406 and/or the second sensor 404 may also be used by the riskybehavior module 408 to observe a second behavior of the first agent at asecond time period.

The risky behavior module 408 may determine whether the observed secondbehavior corresponds to a potential second behavior with a probabilitythat is less than a threshold. Additionally, the risky behavior module408 may use the transceiver 416 and/or the communication module 422 totransmit a warning to a second agent when the probability is less thanthe threshold

FIG. 5 illustrates a flow diagram 500 for transmitting a warningaccording to aspects of the present disclosure. As shown in FIG. 5, atblock 502, an ego agent determines a probability for each one of a setof potential second behaviors of a first agent based on an observedfirst behavior of the first agent at a first time period. At block 504,the ego agent observes a second behavior of the first agent at a secondtime period. the first behavior and second behavior may be observed viaa red-green-blue camera, a RADAR sensor, and/or a LiDAR sensor. Othertypes of sensors are also contemplated.

At block 506, the ego agent determines whether the observed secondbehavior corresponds to a potential second behavior with a probabilitythat is less than a threshold. The probability may be based on theobserved first behavior, environmental conditions, driving regulations,and/or lane geometry. Additionally, at block 508, the ego agenttransmits a warning to a second agent when the probability is less thanthe threshold. The warning may be transmitted via a vehicle-to-vehiclenetwork, a vehicle-to-everything network, a vehicle-to-infrastructurenetwork, a vehicle-to-network network, and/or a vehicle-to-pedestriannetwork. Other types of networks are also contemplated.

In one configuration, the ego agent engages a defensive driving modewhen the probability is less than the threshold. Additionally, oralternatively, the second agent engages a defensive driving mode whenthe probability is less than the threshold. In one configuration, thefirst agent is out of range of a sensor of the second agent.

Based on the teachings, one skilled in the art should appreciate thatthe scope of the present disclosure is intended to cover any aspect ofthe present disclosure, whether implemented independently of or combinedwith any other aspect of the present disclosure. For example, anapparatus may be implemented or a method may be practiced using anynumber of the aspects set forth. In addition, the scope of the presentdisclosure is intended to cover such an apparatus or method practicedusing other structure, functionality, or structure and functionality inaddition to, or other than the various aspects of the present disclosureset forth. It should be understood that any aspect of the presentdisclosure may be embodied by one or more elements of a claim.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any aspect described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother aspects.

Although particular aspects are described herein, many variations andpermutations of these aspects fall within the scope of the presentdisclosure. Although some benefits and advantages of the preferredaspects are mentioned, the scope of the present disclosure is notintended to be limited to particular benefits, uses or objectives.Rather, aspects of the present disclosure are intended to be broadlyapplicable to different technologies, system configurations, networksand protocols, some of which are illustrated by way of example in thefigures and in the following description of the preferred aspects. Thedetailed description and drawings are merely illustrative of the presentdisclosure rather than limiting, the scope of the present disclosurebeing defined by the appended claims and equivalents thereof.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Additionally, “determining” may include receiving (e.g., receivinginformation), accessing (e.g., accessing data in a memory) and the like.Furthermore, “determining” may include resolving, selecting, choosing,establishing, and the like.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a processor specially configured to perform the functionsdiscussed in the present disclosure. The processor may be a neuralnetwork processor, a digital signal processor (DSP), an applicationspecific integrated circuit (ASIC), a field programmable gate arraysignal (FPGA) or other programmable logic device (PLD), discrete gate ortransistor logic, discrete hardware components or any combinationthereof designed to perform the functions described herein.Alternatively, the processing system may comprise one or moreneuromorphic processors for implementing the neuron models and models ofneural systems described herein. The processor may be a microprocessor,controller, microcontroller, or state machine specially configured asdescribed herein. A processor may also be implemented as a combinationof computing devices, e.g., a combination of a DSP and a microprocessor,a plurality of microprocessors, one or more microprocessors inconjunction with a DSP core, or such other special configuration, asdescribed herein.

The steps of a method or algorithm described in connection with thepresent disclosure may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in storage or machine readable medium,including random access memory (RAM), read only memory (ROM), flashmemory, erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), registers, a hard disk,a removable disk, a CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to carry or store desired program code in the form ofinstructions or data structures and that can be accessed by a computer.A software module may comprise a single instruction, or manyinstructions, and may be distributed over several different codesegments, among different programs, and across multiple storage media. Astorage medium may be coupled to a processor such that the processor canread information from, and write information to, the storage medium. Inthe alternative, the storage medium may be integral to the processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

The functions described may be implemented in hardware, software,firmware, or any combination thereof. If implemented in hardware, anexample hardware configuration may comprise a processing system in adevice. The processing system may be implemented with a busarchitecture. The bus may include any number of interconnecting busesand bridges depending on the specific application of the processingsystem and the overall design constraints. The bus may link togethervarious circuits including a processor, machine-readable media, and abus interface. The bus interface may be used to connect a networkadapter, among other things, to the processing system via the bus. Thenetwork adapter may be used to implement signal processing functions.For certain aspects, a user interface (e.g., keypad, display, mouse,joystick, etc.) may also be connected to the bus. The bus may also linkvarious other circuits such as timing sources, peripherals, voltageregulators, power management circuits, and the like, which are wellknown in the art, and therefore, will not be described any further.

The processor may be responsible for managing the bus and processing,including the execution of software stored on the machine-readablemedia. Software shall be construed to mean instructions, data, or anycombination thereof, whether referred to as software, firmware,middleware, microcode, hardware description language, or otherwise.

In a hardware implementation, the machine-readable media may be part ofthe processing system separate from the processor. However, as thoseskilled in the art will readily appreciate, the machine-readable media,or any portion thereof, may be external to the processing system. By wayof example, the machine-readable media may include a transmission line,a carrier wave modulated by data, and/or a computer product separatefrom the device, all which may be accessed by the processor through thebus interface. Alternatively, or in addition, the machine-readablemedia, or any portion thereof, may be integrated into the processor,such as the case may be with cache and/or specialized register files.Although the various components discussed may be described as having aspecific location, such as a local component, they may also beconfigured in various ways, such as certain components being configuredas part of a distributed computing system.

The machine-readable media may comprise a number of software modules.The software modules may include a transmission module and a receivingmodule. Each software module may reside in a single storage device or bedistributed across multiple storage devices. By way of example, asoftware module may be loaded into RAM from a hard drive when atriggering event occurs. During execution of the software module, theprocessor may load some of the instructions into cache to increaseaccess speed. One or more cache lines may then be loaded into a specialpurpose register file for execution by the processor. When referring tothe functionality of a software module below, it will be understood thatsuch functionality is implemented by the processor when executinginstructions from that software module. Furthermore, it should beappreciated that aspects of the present disclosure result inimprovements to the functioning of the processor, computer, machine, orother system implementing such aspects.

If implemented in software, the functions may be stored or transmittedover as one or more instructions or code on a computer-readable medium.Computer-readable media include both computer storage media andcommunication media including any storage medium that facilitatestransfer of a computer program from one place to another.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means, such that a user terminal and/or basestation can obtain the various methods upon coupling or providing thestorage means to the device. Moreover, any other suitable technique forproviding the methods and techniques described herein to a device can beutilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes, and variations may be made in the arrangement, operation, anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

What is claimed is:
 1. A method for identifying low probabilitybehavior, comprising: determining, at an ego agent, a probability foreach one of a set of potential second behaviors of a first agent at asecond time period based on an observed first behavior of the firstagent at a first time period; observing, by the ego agent, a secondbehavior of the first agent at the second time period; determining, atthe ego agent, whether the observed second behavior corresponds to apotential second behavior with a probability that is less than athreshold; and transmitting, from the ego agent, a warning to a secondagent when the probability is less than the threshold.
 2. The method ofclaim 1, further comprising transmitting the warning via at least one ofa vehicle-to-vehicle network, a vehicle-to-everything network, avehicle-to-infrastructure network, a vehicle-to-network network, avehicle-to-pedestrian network, or a combination thereof.
 3. The methodof claim 1, further comprising engaging, at the ego agent, a defensivedriving mode when the probability is less than the threshold.
 4. Themethod of claim 1, in which the second agent engages a defensive drivingmode when the probability is less than the threshold.
 5. The method ofclaim 1, in which the first agent is out of range of a sensor of thesecond agent.
 6. The method of claim 1, in which the probability isbased on at least one of the observed first behavior, environmentalconditions, driving regulations, lane geometry, or a combinationthereof.
 7. The method of claim 1, further comprising observing thefirst behavior and second behavior via at least one of a red-green-bluecamera, a RADAR sensor, a LiDAR sensor, or a combination thereof.
 8. Anapparatus for identifying low probability behavior at an ego agent, theapparatus comprising: a memory; at least one processor coupled to thememory instructions stored in the memory and operable, when executed bythe at least one processor, to cause the apparatus: to determine aprobability for each one of a set of potential second behaviors of afirst agent at a second time period based on an observed first behaviorof the first agent at a first time period; to observe a second behaviorof the first agent at the second time period; to determine whether theobserved second behavior corresponds to a potential second behavior witha probability that is less than a threshold; and to transmit a warningto a second agent when the probability is less than the threshold. 9.The apparatus of claim 8, in which the instructions further cause theapparatus to transmit the warning via at least one of avehicle-to-vehicle network, a vehicle-to-everything network, avehicle-to-infrastructure network, a vehicle-to-network network, avehicle-to-pedestrian network, or a combination thereof.
 10. Theapparatus of claim 8, in which the instructions further cause theapparatus to engage a defensive driving mode when the probability isless than the threshold.
 11. The apparatus of claim 8, in which thesecond agent engages a defensive driving mode when the probability isless than the threshold.
 12. The apparatus of claim 8, in which thefirst agent is out of range of a sensor of the second agent.
 13. Theapparatus of claim 8, in which the probability is based on at least oneof the observed first behavior, environmental conditions, drivingregulations, lane geometry, or a combination thereof.
 14. The apparatusof claim 8, in which the instructions further cause the apparatus toobserve the first behavior and second behavior via at least one of ared-green-blue camera, a RADAR sensor, a LiDAR sensor, or a combinationthereof.
 15. A non-transitory computer-readable medium having programcode recorded thereon for identifying low probability behavior at an egoagent, the program code executed by a processor and comprising: programcode to determine, at the ego agent, a probability for each one of a setof potential second behaviors of a first agent at a second time periodbased on an observed first behavior of the first agent at a first timeperiod; program code to observe, by the ego agent, a second behavior ofthe first agent at the second time period; program code to determine, atthe ego agent, whether the observed second behavior corresponds to apotential second behavior with a probability that is less than athreshold; and program code to transmit, from the ego agent, a warningto a second agent when the probability is less than the threshold. 16.The non-transitory computer-readable medium of claim 15, in which the atleast one processor is further configured to transmit the warning via atleast one of a vehicle-to-vehicle network, a vehicle-to-everythingnetwork, a vehicle-to-infrastructure network, a vehicle-to-networknetwork, a vehicle-to-pedestrian network, or a combination thereof. 17.The non-transitory computer-readable medium of claim 15, in which the atleast one processor is further configured to engage a defensive drivingmode for the ego agent when the probability is less than the threshold.18. The non-transitory computer-readable medium of claim 15, in whichthe second agent engages a defensive driving mode when the probabilityis less than the threshold.
 19. The non-transitory computer-readablemedium of claim 15, in which the first agent is out of range of a sensorof the second agent.
 20. The non-transitory computer-readable medium ofclaim 15, in which the probability is based on at least one of theobserved first behavior, environmental conditions, driving regulations,lane geometry, or a combination thereof.